2 papers
math.OC2026
Using Subproblem Objective Gaps in Inexact Augmented Lagrangian and ADMM Algorithms, with Applications to Stochastic Mixed Integer Programming
Jonathan Eckstein
Through a "partial strong convexity" lemma, this paper shows how bounds on subproblem objective value suboptimality can be used in inexact augmented Lagrangian methods and ADMM alg…
math.OC2025
Two Innovations in Inexact Augmented Lagrangian Methods for Convex Optimization
Jonathan Eckstein, Chang Yu
This paper presents two new techniques relating to inexact solution of subproblems in augmented Lagrangian methods for convex programming. The first involves combining a relative e…